The Right Keyword Bidding Approach

Affiliate Keyword bidding techniques are not created equal. With four incredibly differenttypes of approaches, understanding their differences is a challenge in and ofitself. But knowing the differences and applying the correct approach to your paidsearch program and campaign goals can translate into significant performancegains.

Now the question is when and how to use the choices available to you. Over the past few years, digital marketing platforms and consultants have helpedadvertisers optimize paid search spend using a combination of human analystsand rules and model-based bid optimization software. In this case, technologyhas played a major role in scaling the challenge of daily bidding on tens ofthousands to millions of keywords, from the head to the extreme tail. However,there is still confusion around when to use rules-based bidding or the three majormodel-based bidding techniques: local optimization, global cluster modelingand global keyword-level modeling. Well explore the differences between eachapproach and examine the outcomes.

Rules-Based Bidding

Rules-based bidding is all about reacting to situations, rather than predicting andadjusting by learning from historical conversion data. Advertisers who foregousing tail terms and instead focus only on head terms often use rules-basedbidding. This strategy works when youre only dealing with a few keywords andhave the resources to leverage human analysis, versus optimization software.Of course, this method can only scale so far, as it requires paid searchprofessionals to provide analysis and crunch the numbers on the fly.

Local Optimization

With model-based approaches, its important to make the distinction betweenlocal and global optimization. Local optimization bids each keyword so itachieves the goal in an SEM program, while global optimization (sometimesreferred to as a portfolio approach) considers all keywords at once. Using theglobal approach, bids are assigned so that, on average, the whole group ismaximized for a goal. The global approach usually provides higher value froma set of keywords, compared to local optimization, because with local, somekeywords are overbid, while others are underbid to achieve the goal, and thatresults in poor financial performance.

Global Optimization: Cluster-Modeling

Within global keyword modeling there are two approaches: cluster-basedmodeling and keyword-level modeling. Cluster-based modeling was developedto cope with the data problem surrounding tail terms. Because there was verylittle historic data for these terms, clusters aggregated data from hundreds oreven thousands of keywords, allowing analysts to apply traditional statistical techniques to determine bids. Keyword clusters lead to prediction stability however, each keyword is actually unique, which ultimately results in a loss ofperformance when compared to modeling each keyword individually.

Clustering has another consideration the manual tuning needed to optimizethe keyword clusters means human analysis is needed on a regular basis on topof the software required to automate the bidding for the clusters. Also, its verytime-intensive to create the clusters to begin with. In fact, this is the biggest issue, becauseas a cluster ages, performance decays. Combatting this becomes very expensive.

Global Optimization: Keyword-Level Modeling

As mentioned before, keyword-level modeling is another option for advertisers however, many believed it couldnt be done, because there wasnt enough tailterm behavior data available to build accurate models. This is no longer the case.Keyword-level modeling is possible, due to optimization technology developmentsand sophisticated mathematical techniques.

Leveraging advanced math, software automation and transparency into specificvariables that drive individual keyword performance, advertisers can createmodels for all keywords in an SEM program, not just the low-hanging fruit ofhead terms. This can result in overall performance gains of over 25 percentcompared to clustering, local optimization and rules-based techniques.

Meanwhile, advertisers who arent operating with constraints can leverage localoptimization. If theres an explicit goal for example, to drive as many orders as possible,without regard to cost or profit local optimization will achieve those results. Granted, most advertisers do have constraints, which makes global clusters analternative if advertisers only need to track conversions at the campaign level.

For advertisers who want more transparency and deal with large numbersof keywords, keyword-level modeling provides the opportunity to automatebidding while avoiding the performance and maintenance issues associatedwith clustering. Because this type of modeling provides transparency into thevariables that drive keyword performance, keyword-level modeling can result in dramatic campaign gains when dealing with tens of thousands to millions ofkeywords, whether theyre head or tail terms. Compared to the other keywordbidding techniques, keyword-level modeling is ideal for advertisers looking tomaximize both head and tail terms, while having transparence and control over aSEM program.

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